Open AccessOn the Use of Maximum Likelihood and Input Data Similarity to Obtain Prediction Intervals for Forecasts of Photovoltaic Power Generation
Author(s) -
Joao Gari da Silva Fonseca,
Takashi Oozeki,
Hideaki Ohtake,
Takumi Takashima,
Kazuhiko Ogimoto
Publication year - 2015
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2015.10.3.1342
Subject(s) - photovoltaic system , gaussian , similarity (geometry) , prediction interval , power (physics) , statistics , computer science , mathematics , econometrics , engineering , artificial intelligence , physics , quantum mechanics , electrical engineering , image (mathematics)
The objective of this study is to propose a method to calculate prediction intervals for oneday-ahead hourly forecasts of photovoltaic power generation and to evaluate its performance. One year of data of two systems, representing contrasting examples of forecast’ accuracy, were used. The method is based on the maximum likelihood estimation, the similarity between the input data of future and past forecasts of photovoltaic power, and on an assumption about the distribution of the error of the forecasts. Two assumptions for the forecast error distribution were evaluated, a Laplacian and a Gaussian distribution assumption. The results show that the proposed method models well the photovoltaic power forecast error when the Laplacian distribution is used. For both systems and intervals calculated with 4 confidence levels, the intervals contained the true photovoltaic power generation in the amount near to the expected one.